1.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Chinese Academy of SciencesCN72 papers
- Chinese Academy of SciencesCN70 papers
- Institute of AutomationCN57 papers
- Beijing Academy of Artificial IntelligenceCN20 papers
- Institute of Computing TechnologyCN11 papers
- Shandong Institute of AutomationCN11 papers
- University of Science and Technology of ChinaCN5 papers
- Peng Cheng LaboratoryCN4 papers
- Tsinghua UniversityCN4 papers
- Horizon Robotics (China)3 papers
- Ningbo Institute of Industrial TechnologyCN3 papers
- Tongji UniversityCN3 papers
13 papers · 1 filter
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Yang Li, Yiting Dong, Dongcheng Zhao +1
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet diffi…
AI in Human-computer Gaming: Techniques, Challenges and Opportunities
Qiyue Yin, Jun Yang, Kaiqi Huang +6
With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all around the world. As a recognized standard for t…
In-memory computing based on all-optically controlled memristor
Jing Yang, Lingxiang Hu, Liufeng Shen +5
Artificial intelligence is widely used in everyday life. However, an insufficient computing efficiency due to the so-called von Neumann bottleneck cannot satisfy the demand for rea…
Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment
Jiaming Guo, Rui Zhang, Xishan Zhang +6
Policy gradient methods are appealing in deep reinforcement learning but suffer from high variance of gradient estimate. To reduce the variance, the state value function is applied…
Neighbor-view Enhanced Model for Vision and Language Navigation
Dong An, Yuankai Qi, Yan Huang +3
Vision and Language Navigation (VLN) requires an agent to navigate to a target location by following natural language instructions. Most of existing works represent a navigation ca…
Locality-aware Channel-wise Dropout for Occluded Face Recognition
Mingjie He, Jie Zhang, Shiguang Shan +3
Face recognition remains a challenging task in unconstrained scenarios, especially when faces are partially occluded. To improve the robustness against occlusion, augmenting the tr…
Graph Jigsaw Learning for Cartoon Face Recognition
Yong Li, Lingjie Lao, Zhen Cui +2
Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse a…
GA-NET: Global Attention Network for Point Cloud Semantic Segmentation
Shuang Deng, Qiulei Dong
How to learn long-range dependencies from 3D point clouds is a challenging problem in 3D point cloud analysis. Addressing this problem, we propose a global attention network for po…
Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network
Guo-Wang Xie, Fei Yin, Xu-Yao Zhang +1
As camera-based documents are increasingly used, the rectification of distorted document images becomes a need to improve the recognition performance. In this paper, we propose a n…
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
Transducer-based models, such as RNN-Transducer and transformer-transducer, have achieved great success in speech recognition. A typical transducer model decodes the output sequenc…
TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao +4
The autoregressive (AR) models, such as attention-based encoder-decoder models and RNN-Transducer, have achieved great success in speech recognition. They predict the output sequen…
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling
Li Yang, Yan Xu, Shaoru Wang +4
Recent one-stage object detectors follow a per-pixel prediction approach that predicts both the object category scores and boundary positions from every single grid location. Howev…
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there…